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How to use _save_logs method in lisa

Best Python code snippet using lisa_python

fab_dav_q.py

Source:fab_dav_q.py

...144#145# @hosts(mq_host + cafecommander_host)146# def get_cpu(interval=10, count=30):147#     ret = run('sar -u %d %d' % (interval, count))148#     _save_logs('%s_cpu.log' % env.host, ret)149#150#151# @hosts(mq_host + cafecommander_host)152# def get_mem(interval=10, count=30):153#     ret = run('sar -r %d %d' % (interval, count))154#     _save_logs('%s_mem.log' % env.host, ret)155#156#157# @hosts(mq_host + cafecommander_host)158# def get_load(interval=10, count=30):159#     ret = run('sar -q %d %d' % (interval, count))160#     _save_logs('%s_load.log' % env.host, ret)161#162#163# def _save_logs(logname, content):164#     dirname = 'logs'165#     local('mkdir -p %s' % dirname)166#     logname = os.path.join(dirname, logname)167#     with open(logname, 'w') as f:...

experiment.py

Source:experiment.py

...27        def evaluate_metrics(samples, sample_time, suffix):28            samples = samples[self.args['burn_in']:]29            np.save(os.path.join(self.logs['results'], 'samples_%s.npy' % suffix),30                    samples)31            self._save_logs('results.txt', 'sample time %s' % suffix, sample_time)32            print('evaluating samples')33            bmess = batch_means_ess(samples)34            min_bmess = np.mean(np.min(bmess, axis=1), axis=0)35            std_bmess = np.std(np.min(bmess, axis=1), axis=0)36            gr = gelman_rubin_diagnostic(samples)37            acc_rate = acceptance_rate_2(samples)38            self._save_logs('results.txt', 'num_samples_%s:' % suffix, samples.shape[0])39            self._save_logs('results.txt', 'ess_%s' % suffix, bmess)40            self._save_logs('results.txt', 'min_ess_%s' % suffix, min_bmess)41            self._save_logs('results.txt', 'std_ess_%s' % suffix, std_bmess)42            self._save_logs('results.txt', 'gelman_rubin_%s:' % suffix, gr)43            self._save_logs('results.txt', 'acceptance_rate_%s:' % suffix, acc_rate)44            self._trace_plot(samples)45            eval, eval_std = self._evaluate(samples)46            self._save_logs('results.txt', 'eval_metric_%s' % suffix, eval)47            self._save_logs('results.txt', 'eval_metric_std_%s' % suffix, eval_std)48        print('starting training')49        losses, train_time = self.sampler.train(**self.args)50        np.save(os.path.join(self.logs['results'], 'losses.npy'), np.array(losses))51        self._plot(losses[1:], 'losses.png')52        self._save_logs('results.txt', 'train time', train_time)53        print('drawing samples')54        samples, sample_time = self.sampler.sample(**self.args)55        samples_irr, sample_time_irr = self.sampler.sample_irr(**self.args)56        evaluate_metrics(samples, sample_time, '')57        evaluate_metrics(samples_irr, sample_time_irr, 'irr')58        if samples.shape[2] == 2:59            self._plot2d(samples)60    def _trace_plot(self, samples):61        i = np.random.randint(0, samples.shape[1])62        fig, ax = plt.subplots()63        ax.plot(samples[:, i, :], alpha=0.4)64        ax.plot(samples[:, i, :], '+')65        ax.set_xlim([0, samples.shape[0]])66        ax.set_aspect('auto', 'datalim')67        plt.savefig(os.path.join(self.logs['figs'], 'trace.png'),68                    bbox_inches='tight')69        plt.close()70    def _plot2d(self, samples):71        fig, ax = plt.subplots()72        ax.hist2d(samples[:, 0, 0], samples[:, 0, 1], bins=200)73        ax.set_aspect('equal', 'box')74        plt.savefig(os.path.join(self.logs['figs'], 'samples.png'))75        plt.close()76    def _plot(self, data, title):77        fig, ax = plt.subplots()78        ax.set_aspect('auto', 'datalim')79        ax.plot(data)80        plt.savefig(os.path.join(self.logs['figs'], title), bbox_inches='tight')81        plt.close()82    def _evaluate(self, samples):83        T, M, D = samples.shape84        eval = []85        for i in range(M):86            eval.append(self.dist.evaluate(samples[:, i, :], self.sess))87        return np.mean(eval), np.std(eval)88    def _save_params(self, my_dict):89        my_dict = {k: str(v) for k, v in my_dict.items()}90        with open(os.path.join(self.logs['info'], 'params.txt'), 'w') as f:91            json.dump(my_dict, f,  indent=4)92    def _save_logs(self, filename, savestring, arg):93        with open(os.path.join(self.logs['results'], filename), 'a') as f:...

train.py

Source:train.py

...50            t_start = time.time()51        # Save52        if (itr+1) % args.save_interval == 0:53            saver.save(sess, args.path['model'] + '/itr-' + str(itr))54            _save_logs(os.path.join(args.path['log'], 'train.pickle'), logs['train'])55            _save_logs(os.path.join(args.path['log'], 'val.pickle'), logs['val'])56def _update_logs(logs, log):57    for key in logs.keys():58        logs[key].extend([log[key]])59    return logs60def _save_logs(filename, results):61    with open(filename, 'wb') as f:...

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